Prospective risk estimates confirm that BRCA1 and BRCA2 carriers are at high risk of developing breast, ovarian, and contralateral breast cancer. Our results confirm findings from retrospective studies that common breast cancer susceptibility alleles in combination are predictive of breast cancer risk for BRCA2 carriers.

Background Previous small studies found that BRCA1 and BRCA2 breast tumors differ in their pathology. Analysis of larger datasets of mutation carriers should allow further tumor characterization. Methods We used data from 4,325 BRCA1 and 2,568 BRCA2 mutation carriers to analyze the pathology of invasive breast, ovarian and contralateral breast cancers. Results There was strong evidence that the proportion of estrogen receptor (ER)-negative breast tumors decreased with age at diagnosis among BRCA1 (p-trend=1.2×10−5) but increased with age at diagnosis among BRCA2 carriers (p-trend=6.8×10−6). The proportion of triple negative tumors decreased with age at diagnosis in BRCA1 carriers but increased with age at diagnosis of BRCA2 carriers. In both BRCA1 and BRCA2 carriers, ER-negative tumors were of higher histological grade than ER-positive tumors (Grade 3 vs. Grade 1, p=1.2×10−13 for BRCA1 and p=0.001 for BRCA2). ER and progesterone receptor (PR) expression were independently associated with mutation carrier status (ER-positive odds ratio (OR) for BRCA2=9.4, 95%CI:7.0-12.6 and PR-positive OR=1.7, 95%CI:1.3-2.3, under joint analysis). Lobular tumors were more likely to be BRCA2-related (OR for BRCA2=3.3, 95%CI:2.4-4.4, p=4.4×10−14), and medullary tumors BRCA1-related (OR for BRCA2=0.25, 95%CI:0.18-0.35, p=2.3×10−15). ER-status of the first breast cancer was predictive of ER-status of asynchronous contralateral breast cancer (p=0.0004 for BRCA1; p=0.002 for BRCA2). There were no significant differences in ovarian cancer morphology between BRCA1 and BRCA2 carriers (serous:67%; mucinous:1%; endometriod:12%; clear-cell:2%). Conclusions/Impact Pathology characteristics of BRCA1 and BRCA2 tumors may be useful for improving risk prediction algorithms and informing clinical strategies for screening and prophylaxis.